My apologies. I don't like it when people use word-salad to strong-arm a debate, I just meant to explain the situation from the trenches.

I want to preface by saying: I'm one of the most optimistic AI people you'll meet. I call their current work "creative". I think they'll be conscious; I think they already are. The sky's the limit. But when it comes to getting training data (their food), I wonder if it's a conceptual (not skill-based) impossibility. Think of humans farming, a necessary evil. We transcended the animal kingdom, but we need them for our "foundation" (sustenance). Only recently are we creating synthetic food (eg lab-grown meat), so maybe data-labelling isn't impossible in the final analysis. Or maybe AI transcends supervised learning to unsupervised / semi-supervised (the equivalent of us transcending calories). We'll see, I'm just spit-balling what seems to be a chicken/egg issue; not a capacity issue.

I'll give you an example of the best we currently have with transfer learning (AI -> AI) in NLP. DistillBERT is a slimmed down language model equivalent of its more powerful counterpart (whatever it is you choose as that counterpart model you're trying to approximate, in the BERT family). The original "lossless" model is called the teacher, and the new "lossy" model is called the "student". It's like zipping a model, basically. They way it works is the teacher is trained on a human-created dataset. It does its thing, the student watches it in action (inference) and tries to learn the heuristics without learning the details.

But even the teacher needed human-created training data.

Closer to your "policing" analogy, these creativity-based models (like DALLE-2, Stable Diffusion, etc) are called Generative Adversarial Networks - or just Generative models. They use an Actor/Critic paradigm, where one half of the model (the actor, think right hemisphere) creates the art; and the other half (the critic, think left hemisphere) judges that as legit or sloppy. It's actually trying to judge it as real (human-created) or not (AI-created). So that's closer to your policing analogy. HOWEVER! Even here, the actor fully required human art to train. In no way could it have bootstrapped even a little without the original dataset of human art. But now it can take its training wheels off, and away it goes.

Any way you spin AI->AI training, these things have names and they're not what you think. Actor/critic, distillation, transfer learning, zero-shot learning, few-shot learning, etc. The absolute closest to what you're implying is zero-shot learning, and they way that works is by taking a trained model in one domain, have it predict in this new domain (based on its irrelevant skill), make an analogy from that to this domain, and use that as training data. Per previous, a common example is this. New domain is classification (cat, dog, or tree). Old domain is next-word prediction (masked language modeling), eg "I like when my [MASK] purrs". Predict the mask for the current text, use as training data to train a classifier. But again.... seeing the problem yet?

What we'd need is a full switch from supervised learning to reinforcement learning models running shop, which are learning in the world from their own experience, to provide the training data for any supervised learning models left around.

I'm not doubting its capacity for creativity, this is something different. Providing the data which it uses to learn. Snake eating its tail, chicken/egg. Think of asking a student to write the study material from which they'll learn.

One job I imagine being introduced is data-labeling. Humans provide the food AI uses to function: data (for training). Eg, StableDiffusion doesn't work without its art dataset.

Already there's an Amazon Web Services (most common web hosting service) called SageMaker Ground Truth Plus, described as "labeling workforces." Almost like call centers but for labeling data for customers / companies. Eg, AWS provides a UI for labelers to draw bounding boxes around a cat in an image the type in "cat". That goes back to the company's ML model for fine-tuning.

It's a depressing new job, honestly. The burger-flipping / call-center equivalent in 2023: click, type, enter. Click, type, enter. The reason I believe only humans can do this, is because it's where humans end and AI starts. It's the bottom turtle. Like, it's a snake eating its own tail to have AI label its own dataset; recursion error. Not just that it'd do a poor job compared to humans (that's arguable); but that it doesn't make sense / compute. The closest way to achieve this AI->AI, is via something called Zero Shot Learning; where a model from a different domain (say next-word-prediction, or "masked language model") is used to create data for this domain (eg, classification: by predicting a word, which ends up being the class). But even still, eventually it's the recursion problem - I really think humans are needed for this.

So there you have it. Data labeling workforces, and Amazon got a jump on that via SageMaker Ground Truth - providing the onboarding, UI, security / privacy, etc.